Predicting Antigen Presentation-What Could We Learn From a Million Peptides?

Abstract

Antigen presentation lies at the heart of immune recognition of infected or malignant cells. For this reason, important efforts have been made to predict which peptides are more likely to bind and be presented by the human leukocyte antigen (HLA) complex at the surface of cells. These predictions have become even more important with the advent of next-generation sequencing technologies that enable researchers and clinicians to rapidly determine the sequences of pathogens (and their multiple variants) or identify non-synonymous genetic alterations in cancer cells. Here, we review recent advances in predicting HLA binding and antigen presentation in human cells. We argue that the very large amount of high-quality mass spectrometry data of eluted (mainly self) HLA ligands generated in the last few years provides unprecedented opportunities to improve our ability to predict antigen presentation and learn new properties of HLA molecules, as demonstrated in many recent studies of naturally presented HLA-I ligands. Although major challenges still lie on the road toward the ultimate goal of predicting immunogenicity, these experimental and computational developments will facilitate screening of putative epitopes, which may eventually help decipher the rules governing T cell recognition.

Keywords: T cell epitope; antigen presentation; computational immunology; human leukocyte antigen ligand prediction; human leukocyte antigen peptidomics.

Figure 1

Analysis of HLA-I and HLA-II ligands obtained from human leukocyte antigen (HLA) peptidomics studies and in vitro assays. The number of unique HLA-I ligands, the number of unique interactions, the number of HLA alleles with at least one ligand, and the number of HLA alleles with at least 100 ligands are displayed for both class I and class II, as a function of years (cumulative distributions).

Figure 2

Hierarchical clustering of HLA-A alleles based on their binding specificity. Stars indicate cases where only in vitro binding data were available to generate the motifs. In all other cases, only mass spectrometry data were used. Name colors and their descriptions in the legend indicate supertypes as defined in Ref. (185).

Figure 3

Hierarchical clustering of HLA-B alleles based on their binding specificity. Stars indicate cases where only in vitro binding data were available to generate the motifs. In all other cases, only mass spectrometry data were used. Name colors and their description in the legend indicate supertypes as defined in Ref. (185).

Figure 4

Hierarchical clustering of HLA-C alleles based on their binding specificity. Only mass spectrometry data were used. Peptides from both single allele and deconvolved pooled HLA peptidomics samples were used (see Supplementary Material).